Field
The disclosed concept pertains generally to electric loads and, more particularly, to methods of identifying electric load types of electric loads. The disclosed concept also pertains to systems for identifying electric load types of electric loads.
Background Information
Electric loads in residential and commercial building sectors account for over 75% of the total electricity consumption in the United States in 2009. Electric loads are commonly divided into several groups, such as for example and without limitation, space conditioning loads, water heating loads, ventilation loads, major appliances, lighting loads, and miscellaneous electric loads (MELs). MELs are the wide and diverse collection of portable and plug-in electricity-consuming devices along with all hard-wired electric loads that do not fit into other categories. TV sets and accessories, computers and accessories, portable equipment using chargers, and kitchen appliances are examples of typical MELs. Compared with any other single major category, MELs currently consume the largest portion of electricity. A recent report from the U.S. Department of Energy (DoE) predicts that MELs consumption will increase by an average of 2.3 percent per year and, in 2035, will account for 40 percent of the total electricity consumption in the commercial sector.
MELs' relatively large portion in electricity consumption leads to increasing needs and opportunities of energy management and saving. A recent U.S. DoE research program, called Building America, aims at 50% energy savings in new homes by 2015 and 100% savings (zero net energy) by 2020. This program has started to identify and develop advanced solutions that can significantly reduce MELs' power consumption. Moreover, granular load energy consumption and performance information is desired to accelerate the path toward smarter building energy intensity reduction, demand response, peak shaving, and energy optimization and saving.
MELs present special challenges because their operations are mainly under the need and control of building occupants. Without advanced control and management, MELs can constitute 30% to 40% of the electricity use in homes. Furthermore, MELs are distinct from other load categories as many MELs are of notable importance in daily life. For instance, a circuit protection device on an uninterruptable power supply (UPS) or at the input to a power strip interrupts all downstream power circuits when an overcurrent fault happens, but such an unexpected power interruption will cause sensitive equipment, such as a plugged-in desktop computer, to lose all current memory-based work.
MELs currently consume more electricity than any other single end-use service. MELs provide granular energy consumption and performance information to meet rising needs and opportunities of energy saving, demand response, peak shaving, and building management. A reliable intelligent method and system to identify different MELs is desired.
Several methods have been proposed to non-intrusively identify electric loads. Such known methods mainly consist of two major categories. Steady-state features, such as instantaneous real power, power factor, V-I trajectory and harmonic components, are extracted from voltage and current measurements. In the first category, some methods compare these features and their variations with a predefined database. In the second category, some methods adopt computational intelligent (CI) algorithms, such as radial basis functions (RBF), particle swarm optimization (PSO) and artificial neural networks (ANN).
The former category has disadvantages in accuracy, robustness and applicability. For instance, different MELs with similar front-end power supply units are not distinguishable in this manner. Also, few of the known methods are specifically designed for MELs. As a result, products on market, such as Navetas™ and enPowerMe™, are restricted to only a limited number of MELs.
The latter category suffers from the lack of knowledge during training and computational cost, which limits its applicability.
Moreover, the rapid development of power supply designs brings challenges to load identification. One type of MEL may be supplied by different power supply topologies. Therefore, a reliable load identification algorithm should be able to identify loads with diverse specifications, such as manufacturer and power rating.
A support vector machine (SVM) is a well known concept in computer science for a set of related supervised learning methods that analyze data and recognize patterns, used for classification and regression analysis. A SVM has discriminative power for static classification problems and the capability to construct flexible decision boundaries. The standard SVM takes a set of input data made up of training examples and predicts, for each given input example, which of two possible classes the input belongs. This makes the SVM a non-probabilistic binary linear classifier. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns the examples into one category or the other. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to one of the categories based on which side of the gap they fall on.
A SVM constructs a hyperplane or set of hyperplanes in a high- or infinite-dimensional space, which can be used for classification, regression, or other tasks. Intuitively, a good separation is achieved by the hyperplane that has the largest distance to the nearest training data points of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier.
Whereas an original problem may be stated in a finite dimensional space, called the original space, it often happens that the sets to be mapped are not linearly separable in that space. For this reason, the original finite-dimensional space is mapped into a much higher-dimensional space, making the separation easier in that space. To keep the computational load reasonable, the mapping used by SVM schemes are designed to ensure that dot products may be computed easily in terms of the variables in the original space, by defining them in terms of a kernel function K(x,y) selected to suit the problem. The hyperplanes in the higher dimensional space are defined as the set of points whose inner product with a vector in that space is constant. The vectors defining the hyperplanes can be chosen to be linear combinations with parameters αi of images of feature vectors that occur in the database. With this choice of a hyperplane, the points x in the feature space that are mapped into the hyperplane are defined by the relation:
            ∑      i                            ⁢                  ⁢                  α        i            ⁢              K        ⁡                  (                                    x              i                        ,            x                    )                      =  constant
If K(x,y) becomes small as y grows further from x, then each element in the sum measures the degree of closeness of the test point x to the corresponding database point xi. In this way, the sum of kernels above can be used to measure the relative nearness of each test point to the data points originating in one or the other of the sets to be discriminated. The set of points x mapped into any hyperplane can be quite convoluted as a result allowing much more complex discrimination between sets which are not convex at all in the original space.
In the case of support vector machines (SVMs), a data point is viewed as a p-dimensional vector (a list of p numbers), and the goal is to separate such points with a (p−1)-dimensional hyperplane. This is called a linear classifier. There are many hyperplanes that might classify the data. One reasonable choice as the best hyperplane is the one that represents the largest separation, or margin, between the two classes. The hyperplane is chosen so that the distance from it to the nearest data point on each side is maximized. If such a hyperplane exists, then it is known as the maximum-margin hyperplane and the linear classifier it defines is known as a maximum margin classifier; or equivalently, the perception of optimal stability.
There is room for improvement in methods of identifying electric load types of a plurality of different electric loads.
There is also room for improvement in systems for identifying electric load types of a plurality of different electric loads.